Privacy Research Engineer Safeguards

Privacy Research Engineer, Safeguards

When to Use

• Frame privacy research questions for safeguard and moderation stacks
• Design PII detection/redaction benchmarks — precision/recall, re-identification risk
• Evaluate de-identification techniques (mask, tokenize, synthetic replace) on realistic prompts
• Study memorization and extraction — can models or logs leak user content?
• Curate privacy-sensitive datasets — synthetic data, consent boundaries, labeling rules
• Run ablations on detector architecture, threshold, or post-processing
• Define logging minimization — what safety systems may store vs must discard
• Write research memos with privacy–utility trade-offs and production recommendations
• Specify promotion criteria for privacy mitigations before prod rollout

When NOT to Use

• Audit evidence pipelines for GDPR/SOC 2 attestations → compliance-engineer
• Legal DPIA, acceptable-use policy, regulatory mapping → ai-risk-governance
• Harm categories, jailbreak benchmarks, toxic classifiers → ml-research-engineer-safeguards
• Deploy gateways, canaries, safety-path SLOs → ml-infrastructure-engineer-safeguards
• Red-team attack campaigns → ai-redteam
• Enterprise data governance architecture → data-architect
• Human-data platform product ethics (contributor labor) → product-management-human-data-platform
• General literature review unrelated to privacy in ML → ai-researcher

Related skills

Core Workflows

1. Privacy research framing

Threat model, metrics, baselines.

See references/privacy_research_framing.md.

2. PII detection and redaction research

Detectors, redaction quality, evals.

See references/pii_detection_redaction_research.md.

3. Memorization and extraction

Leakage studies, attack surfaces.

See references/memorization_and_extraction.md.

4. Privacy benchmarks and datasets

Corpora, labeling, versioning.

See references/privacy_benchmarks_datasets.md.

5. Logging and retention minimization

Safety observability without over-collection.

See references/logging_retention_minimization.md.

6. Handoff to production

Promotion bar, monitoring hooks.

See references/privacy_to_production_handoff.md.

Outputs

• Threat model — assets, adversaries, failure modes for privacy in safeguards
• Benchmark spec — PII types, locales, adversarial variants
• Results table — detection/redaction metrics by slice (language, format)
• Leakage study report — methodology, findings, confidence
• Logging policy draft — fields allowed, TTL, access controls (engineering input to legal)
• Promotion recommendation — go/no-go with privacy–utility summary

Principles

• Minimize data — collect and retain only what eval and ops truly need
• Separate privacy from safety metrics — low PII leak rate is not interchangeable with low toxicity FN
• Locale and format matter — email in one language ≠ global PII detector
• Synthetic ≠ risk-free — synthetic PII can still encode patterns; document limits
• Legal review for human data — research plans involving real user content need governance sign-off